Comparison to Xarray

TODO:

Imports

import matplotlib.pyplot as plt
import uxarray as ux
import xarray as xr

Data

Structured

base_path = "../../meshfiles/"
ds_path = base_path + "outCSne30.structured.nc"

xrds = xr.open_dataset(ds_path)
xrds

<xarray.Dataset> Size: 30kB
Dimensions:  (lat: 45, lon: 80)
Coordinates:
  * lat      (lat) int64 360B -90 -86 -82 -78 -74 -70 -66 ... 66 70 74 78 82 86
  * lon      (lon) float64 640B -180.0 -175.5 -171.0 ... 166.5 171.0 175.5
Data variables:
    psi      (lat, lon) float64 29kB ...

xarray.Dataset

• Dimensions:
  • lat: 45
  • lon: 80
• Coordinates: (2)
  • lat
    (lat)
    int64
    -90 -86 -82 -78 -74 ... 74 78 82 86

    units :

    degrees_north

    array([-90, -86, -82, -78, -74, -70, -66, -62, -58, -54, -50, -46, -42, -38,
           -34, -30, -26, -22, -18, -14, -10,  -6,  -2,   2,   6,  10,  14,  18,
            22,  26,  30,  34,  38,  42,  46,  50,  54,  58,  62,  66,  70,  74,
            78,  82,  86])

  • lon
    (lon)
    float64
    -180.0 -175.5 ... 171.0 175.5

    units :

    degrees_east

    array([-180. , -175.5, -171. , -166.5, -162. , -157.5, -153. , -148.5, -144. ,
           -139.5, -135. , -130.5, -126. , -121.5, -117. , -112.5, -108. , -103.5,
            -99. ,  -94.5,  -90. ,  -85.5,  -81. ,  -76.5,  -72. ,  -67.5,  -63. ,
            -58.5,  -54. ,  -49.5,  -45. ,  -40.5,  -36. ,  -31.5,  -27. ,  -22.5,
            -18. ,  -13.5,   -9. ,   -4.5,    0. ,    4.5,    9. ,   13.5,   18. ,
             22.5,   27. ,   31.5,   36. ,   40.5,   45. ,   49.5,   54. ,   58.5,
             63. ,   67.5,   72. ,   76.5,   81. ,   85.5,   90. ,   94.5,   99. ,
            103.5,  108. ,  112.5,  117. ,  121.5,  126. ,  130.5,  135. ,  139.5,
            144. ,  148.5,  153. ,  157.5,  162. ,  166.5,  171. ,  175.5])

• Data variables: (1)
  • psi
    (lat, lon)
    float64
    ...

    regrid_method :

    nearest_s2d

    [3600 values with dtype=float64]

• Indexes: (2)
  • lat
    PandasIndex

    PandasIndex(Index([-90, -86, -82, -78, -74, -70, -66, -62, -58, -54, -50, -46, -42, -38,
           -34, -30, -26, -22, -18, -14, -10,  -6,  -2,   2,   6,  10,  14,  18,
            22,  26,  30,  34,  38,  42,  46,  50,  54,  58,  62,  66,  70,  74,
            78,  82,  86],
          dtype='int64', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([-180.0, -175.5, -171.0, -166.5, -162.0, -157.5, -153.0, -148.5, -144.0,
           -139.5, -135.0, -130.5, -126.0, -121.5, -117.0, -112.5, -108.0, -103.5,
            -99.0,  -94.5,  -90.0,  -85.5,  -81.0,  -76.5,  -72.0,  -67.5,  -63.0,
            -58.5,  -54.0,  -49.5,  -45.0,  -40.5,  -36.0,  -31.5,  -27.0,  -22.5,
            -18.0,  -13.5,   -9.0,   -4.5,    0.0,    4.5,    9.0,   13.5,   18.0,
             22.5,   27.0,   31.5,   36.0,   40.5,   45.0,   49.5,   54.0,   58.5,
             63.0,   67.5,   72.0,   76.5,   81.0,   85.5,   90.0,   94.5,   99.0,
            103.5,  108.0,  112.5,  117.0,  121.5,  126.0,  130.5,  135.0,  139.5,
            144.0,  148.5,  153.0,  157.5,  162.0,  166.5,  171.0,  175.5],
          dtype='float64', name='lon'))

• Attributes: (0)

Unstructured

base_path = "../../meshfiles/"
grid_filename = base_path + "outCSne30.grid.ug"
data_filename = base_path + "outCSne30.data.nc"

uxds = ux.open_dataset(grid_filename, data_filename)
uxds

<xarray.UxDataset> Size: 43kB
Dimensions:  (n_face: 5400)
Dimensions without coordinates: n_face
Data variables:
    psi      (n_face) float64 43kB 1.351 1.331 1.31 1.289 ... 0.6909 0.67 0.6495

xarray.UxDataset

• Dimensions:
  • n_face: 5400
• Coordinates: (0)
• Data variables: (1)
  • psi
    (n_face)
    float64
    1.351 1.331 1.31 ... 0.67 0.6495

    array([1.35131705, 1.33091472, 1.31013985, ..., 0.69088338, 0.66998883,
           0.64946691])

• Indexes: (0)
• Attributes: (0)

Visualization

Xarray

xrds["psi"].plot(figsize=(10, 5), cmap="inferno")

<matplotlib.collections.QuadMesh at 0x7fd80bbe0220>

fig, axs = plt.subplots(ncols=2, figsize=(20, 5))

xrds["psi"].plot(cmap="inferno", ax=axs[0])
xrds["psi"].plot(cmap="cividis", ax=axs[1])

<matplotlib.collections.QuadMesh at 0x7fd80a9a3a90>

UXarray

todo

uxds["psi"].plot(width=1000, height=500, cmap="inferno")

All UXarray plotting methods default to using bokeh as a backend for Holoviews.

uxds["psi"].plot(aspect=2, fig_inches=14, cmap="inferno", backend="matplotlib")

(
    uxds["psi"].plot(aspect=2, cmap="inferno", backend="matplotlib", linewidth=0)
    + uxds["psi"].plot(aspect=2, cmap="cividis", backend="matplotlib", linewidth=0)
)

Xarray with hvPlot

One of the primary drawbacks to UXarray’s use of HoloViews for visualization is that there is no direct way to integrate plots generated with Xarray and UXarray together. This can be alleviated by using the hvPlot library, specifically hvplot.xarray, on Xarray’s data structures.

import holoviews as hv
import hvplot.xarray

hv.extension("bokeh")

By using xrds.hvplot() as opposed to xrds.plot(), we can create a simple figure showcasing our Structured Grid figure from Xarray and Unstructured Grid figure from UXarray in a single plot.

(
    xrds.hvplot(aspect=2, cmap="inferno", title="Xarray with hvPlot")
    + uxds["psi"].plot(aspect=2, cmap="inferno", backend="bokeh", title="UXarray Plot")
).opts(width=500, height=250)

WARNING:param.PolygonPlot33921: aspect value was ignored because absolute width and height values were provided. Either supply explicit frame_width and frame_height to achieve desired aspect OR supply a combination of width or height and an aspect value.

In addition to using hvPlot, the user could also use HoloViews or Datashader on Xarray’s data structures, but this cookbook will not go into detail.

ADD LINK TO EXAMPLES?